Fuzzy Integral-Based Multi-Classifiers Ensemble for Android Malware Classification

One of the most commonly used operating systems for smartphones is Android. The open-source nature of the Android operating system and the ability to include third-party Android apps from various markets has led to potential threats to user privacy. Malware developers use sophisticated methods that...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Altyeb Taha, Omar Barukab, Sharaf Malebary
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	Android malware classification ensemble learning choquet fuzzy integral Mathematics QA1-939
Acceso en línea:	https://doaj.org/article/fb015018fa704d64a0029fe464c51788
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:fb015018fa704d64a0029fe464c51788
record_format	dspace
spelling	oai:doaj.org-article:fb015018fa704d64a0029fe464c517882021-11-25T18:16:50ZFuzzy Integral-Based Multi-Classifiers Ensemble for Android Malware Classification10.3390/math92228802227-7390https://doaj.org/article/fb015018fa704d64a0029fe464c517882021-11-01T00:00:00Zhttps://www.mdpi.com/2227-7390/9/22/2880https://doaj.org/toc/2227-7390One of the most commonly used operating systems for smartphones is Android. The open-source nature of the Android operating system and the ability to include third-party Android apps from various markets has led to potential threats to user privacy. Malware developers use sophisticated methods that are intentionally designed to bypass the security checks currently used in smartphones. This makes effective detection of Android malware apps a difficult problem and important issue. This paper proposes a novel fuzzy integral-based multi-classifier ensemble to improve the accuracy of Android malware classification. The proposed approach utilizes the Choquet fuzzy integral as an aggregation function for the purpose of combining and integrating the classification results of several classifiers such as XGBoost, Random Forest, Decision Tree, AdaBoost, and LightGBM. Moreover, the proposed approach utilizes an adaptive fuzzy measure to consider the dynamic nature of the data in each classifier and the consistency and coalescence between each possible subset of classifiers. This enables the proposed approach to aggregate the classification results from the multiple classifiers. The experimental results using the dataset, consisting of 9476 Android goodware apps and 5560 malware Android apps, show that the proposed approach for Android malware classification based on the Choquet fuzzy integral technique outperforms the single classifiers and achieves the highest accuracy of 95.08%.Altyeb TahaOmar BarukabSharaf MalebaryMDPI AGarticleAndroid malware classificationensemble learningchoquet fuzzy integralMathematicsQA1-939ENMathematics, Vol 9, Iss 2880, p 2880 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Android malware classification ensemble learning choquet fuzzy integral Mathematics QA1-939
spellingShingle	Android malware classification ensemble learning choquet fuzzy integral Mathematics QA1-939 Altyeb Taha Omar Barukab Sharaf Malebary Fuzzy Integral-Based Multi-Classifiers Ensemble for Android Malware Classification
description	One of the most commonly used operating systems for smartphones is Android. The open-source nature of the Android operating system and the ability to include third-party Android apps from various markets has led to potential threats to user privacy. Malware developers use sophisticated methods that are intentionally designed to bypass the security checks currently used in smartphones. This makes effective detection of Android malware apps a difficult problem and important issue. This paper proposes a novel fuzzy integral-based multi-classifier ensemble to improve the accuracy of Android malware classification. The proposed approach utilizes the Choquet fuzzy integral as an aggregation function for the purpose of combining and integrating the classification results of several classifiers such as XGBoost, Random Forest, Decision Tree, AdaBoost, and LightGBM. Moreover, the proposed approach utilizes an adaptive fuzzy measure to consider the dynamic nature of the data in each classifier and the consistency and coalescence between each possible subset of classifiers. This enables the proposed approach to aggregate the classification results from the multiple classifiers. The experimental results using the dataset, consisting of 9476 Android goodware apps and 5560 malware Android apps, show that the proposed approach for Android malware classification based on the Choquet fuzzy integral technique outperforms the single classifiers and achieves the highest accuracy of 95.08%.
format	article
author	Altyeb Taha Omar Barukab Sharaf Malebary
author_facet	Altyeb Taha Omar Barukab Sharaf Malebary
author_sort	Altyeb Taha
title	Fuzzy Integral-Based Multi-Classifiers Ensemble for Android Malware Classification
title_short	Fuzzy Integral-Based Multi-Classifiers Ensemble for Android Malware Classification
title_full	Fuzzy Integral-Based Multi-Classifiers Ensemble for Android Malware Classification
title_fullStr	Fuzzy Integral-Based Multi-Classifiers Ensemble for Android Malware Classification
title_full_unstemmed	Fuzzy Integral-Based Multi-Classifiers Ensemble for Android Malware Classification
title_sort	fuzzy integral-based multi-classifiers ensemble for android malware classification
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/fb015018fa704d64a0029fe464c51788
work_keys_str_mv	AT altyebtaha fuzzyintegralbasedmulticlassifiersensembleforandroidmalwareclassification AT omarbarukab fuzzyintegralbasedmulticlassifiersensembleforandroidmalwareclassification AT sharafmalebary fuzzyintegralbasedmulticlassifiersensembleforandroidmalwareclassification
_version_	1718411417115164672

Fuzzy Integral-Based Multi-Classifiers Ensemble for Android Malware Classification

Ejemplares similares